BigML Bindings: 101 - Using a Topic Model

Following the schema described in the prediction workflow, document, this is the code snippet that shows the minimal workflow to create a topic model and produce a single topic distribution.

from bigml.api import BigML
# step 0: creating a connection to the service (default credentials)
api = BigML()
# step 1: creating a source from the data in your local "data/spam.csv" file
source = api.create_source("data/spam.csv")
# waiting for the source to be finished. Results will be stored in `source`
api.ok(source)
# step 3: creating a dataset from the previously created `source`
dataset = api.create_dataset(source)
# waiting for the dataset to be finished
api.ok(dataset)
# step 5: creating a topc model
topic_model = api.create_topic_model(dataset)
# waiting for the topic model to be finished
api.ok(topic_model)
# the new input data to predict for
input_data = {"Message": "Mobile offers, 20% discount."}
# creating a single topic distribution
topic_distribution = api.create_topic_distribution(topic_model, input_data)

Remember that your dataset needs to have at least a text field to be able to create a topic model. If you want to create topic distributions for many new inputs, you can do so by creating a batch_topic_distribution resource. First, you will need to upload to the platform all the input data that you want to use for and create the corresponding source and dataset resources. In the example, we’ll be assuming you already created a topic model following the steps 0 to 5 in the previous snippet.

# step 6: creating a source from the data in your local "data/test_spam.csv" file
test_source = api.create_source("data/test_spam.csv")
# waiting for the source to be finished. Results will be stored in `source`
api.ok(test_source)
# step 8: creating a dataset from the previously created `source`
test_dataset = api.create_dataset(test_source)
# waiting for the dataset to be finished
api.ok(test_dataset)
# step 10: creating a batch topic distribution
batch_topic_distribution = api.create_batch_topic_distribution( \
    topic_model, test_dataset)
# waiting for the batch_topic_distribution to be finished
api.ok(batch_topic_distribution)
# downloading the results to your computer
api.download_batch_topic_distribution( \
    batch_topic_distribution, filename='my_dir/my_predictions.csv')

The batch topic distribution output (as well as any of the resources created) can be configured using additional arguments in the corresponding create calls. For instance, to include all the information in the original dataset in the output you would change step 10 to:

batch_topic_distribution = api.create_batch_topic_distribution( \
    topic_model, test_dataset, {"all_fields": True})

Check the API documentation to learn about the available configuration options for any BigML resource.

You can also predict locally using the TopicModel class in the topicmodel module. A simple example of that is:

from bigml.topicmodel import TopicModel
local_topic_model = TopicModel("topicmodel/5968ec46983efc21b000001b")
# topic distribution for some input data
local_topic_model.distribution({"Message": "Mobile offers, 20% discount."})

Or you could store first your topic model information in a file and use that file to create the local TopicModel object:

# downloading the topic model JSON to a local file
from bigml.api import BigML
api = BigML()
api.export("topicmodel/5968ec46983efc21b000001b",
           "filename": "my_topic_model.json")
# creating the topic model from the file
from bigml.topicmodel import TopicModel
local_topic_model = TopicModel("my_topic_model.json")
# topic distribution for some input data
local_topic_model.distribution({"Message": "Mobile offers, 20% discount."})

And if you want to predict locally for all the rows in a CSV file (first line should contain the field headers):

import csv
from bigml.topicmodel import TopicModel
local_topic_model = TopicModel("topicmodel/5a414c667811dd5057000ab5")
with open("test_data.csv") as test_handler:
    reader = csv.DictReader(test_handler)
    for input_data in reader:
    # predicting for all rows
        print local_topic_model.distribution(input_data)

Every modeling resource in BigML has its corresponding local class. Check the Local resources section of the documentation to learn more about them.